With the proliferation of mobile devices (e.g., smartphones, tablets, etc.), the average person now owns multiple devices (e.g., averaging upwards of three in the USA, according to some surveys) and is using these devices at different times of the day for various online activities (e.g., work, email, web browsing, online shopping, watching TV, watching movies, etc.). These same device users can also have one or more devices at home (e.g., laptop computer, desktop computer, internet TV, etc.) that they further use for additional activities. To improve the effectiveness (e.g., conversion rate, optimized media spend, etc.) of online advertising campaigns, advertisers have a need to associate a given user with as many (or all) of the devices he or she may use in order to more confidently know or predict the interests of that user, so as to reach that user with targeted advertising (e.g., a “cross-device campaign”). Associating a set of devices with a given user is referred to herein as cross-device matching.
One legacy approach is to capture and associate browser cookie data with users who have logged into specific online accounts on multiple devices, yet advertisers want to retarget users who have expressed interest through user actions taken using multiple devices—even when none of the multiple devices support browser cookies.
To allow advertisers to take advantage of all available user profile information (e.g., categories) when performing cross-platform and cross-device user advertising targeting, there is a need for matching users across multiple devices and providing a cross-device map that serves to synchronize user categories (e.g., behaviors, buying interests, etc.) so as to improve the makeup of targeted audiences (e.g., to reach to same user on that user's many devices). What is needed is a technique or techniques to improve the application and efficacy of various technologies as compared with the application and efficacy of legacy approaches.